1. Field
This invention relates to a method and system for identifying spurious regions in a video frame, particularly a video frame comprising part of a video sequence.
2. Description of Related Art
Digital video processing is used in a wide range of applications. For example, modern video surveillance systems employ digital processing techniques to provide information concerning moving objects in the video. Such a system will typically comprise a video camera connected to a computer system via a direct or network link. The computer system runs software arranged to process and analyse video data supplied from the camera.
FIG. 1 is a block diagram showing the software-level stages of such a surveillance system. In the first stage 1, a background model is learned from an initial segment of video data. The background model typically comprises statistical information representing the relatively static background content. In this respect, it will be appreciated that a background scene will remain relatively stationary compared with objects in the foreground. In a second stage 3, foreground extraction and background adaptation is performed on each incoming video frame. The current frame is compared with the background model to estimate which pixels of the current frame represent foreground regions and which represent background. Small changes in the background model are also updated. In a third stage 5, foreground regions are tracked from frame to frame and a correspondence is established between foreground regions in the current frame and those tracked in previous frames. Meanwhile a trajectory database is updated so that the tracking history of each foreground region is available to higher-level applications 7 which may, for example, perform behavioural analysis on one or more of the tracked objects.
After processing each video frame, a validity check 9 is performed on the background model to determine whether it is still valid. Significant or sudden changes in the captured scene may require initialisation of a new background model by returning to the first stage 1.
A known intelligent video system is disclosed in US Patent Application Publication No. 2003/0053659 A1. A known foreground extraction and tracking method is disclosed by Stauffer and Grimson in “Learning Patterns of Activity using Real-Time Tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22, No. 8, August 2000.
In the foreground extraction stage 3, it is common for some image regions to be classified as foreground objects when, in fact, this is not the case. For example, if a video scene contains repetitive motion, such as leaves waving back and forth on a tree, the foreground extraction stage 3 may classify the moving region as foreground when, in fact, the leaves form part of the background scene. In addition, the process of capturing, encoding and decoding video data will inevitably introduce noise to the system. It is possible that this noise will be detected as foreground by the inherent operation of the foreground extraction stage 3. Such incorrectly classified image regions are considered, and referred to herein, as spurious regions.
It is desirable to identify such spurious regions in video frames. In this way, it is possible to disregard these regions for the purposes of subsequent processing steps intended to be performed on true regions of interest. For example, it is desirable for the object tracking stage 5 to operate on real foreground regions only. By attempting to track regions representing repetitive motion or noise, the video processing system wastes valuable processing and memory resources on data which is of no interest to a user.